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On the Generation of an Optimized Fractional Cloudiness Time Series using a Multi-Sensor Approach

On the Generation of an Optimized Fractional Cloudiness Time Series using a Multi-Sensor Approach. Wiel Wauben * , Marijn de Haij Reinout Boers, Henk Klein Baltink, Bert van Ulft, Mark Savenije * R&D Information and Observation Technology, Climate Observations Dept,

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On the Generation of an Optimized Fractional Cloudiness Time Series using a Multi-Sensor Approach

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  1. On the Generation of an Optimized Fractional Cloudiness Time Series using a Multi-Sensor Approach Wiel Wauben*, Marijn de Haij Reinout Boers, Henk Klein Baltink, Bert van Ulft, Mark Savenije *R&D Information and Observation Technology, Climate Observations Dept, Regional Climate Dept, Weather Research and Development Dept

  2. Contents Introduction Instruments Combination algorithm Cabauw Fractional Cloudiness Conclusions and outlook

  3. Cabauw Experimental Site for Atmospheric Research Five remote sensing techniques for cloud observations Active and passive Column and hemispheric (integrated and resolved/scanning) 1 year data sets of 10-minute cloud data (15 May 2008 - 14 May 2009, total cloud cover & base) Generation of optimized & continuous cloudiness time series Evaluation of different techniques CESAR

  4. 35 GHz cloud radar & CT75K Sensitive to detect high cirrus CLOUDNET procedure column techniques including cloud base height Instruments Ceilometer (operational SYNOP/METAR cloud product)

  5. Instruments long-wave downward radiation integrated hemispheric APCADA algorithm thermal infrared scanning cloud mask visual digital camera cloud mask day-time only Pyrgeometer (BSRN) NubiScope Total sky imager (TSI)

  6. Combination Goal: construction of optimized & continuous cloudiness time series strong / weak points situation dependent subjective no reference! complex algorithm not generic “simple” weightedaverage based on experiences checked with climatologyof manual observations (1970-2000) Manual approach Hence

  7. Combination Rj is the reference cloudiness (in percentage) at time j Wi,j is the weighting value at time j for the i-th instrument Hi,j=1 when the i-th instrument has a valid output at time j, else =0 Ci,j is the cloudiness (in percentage) measured by the i-th instrument at time j WNUB,j = WTSI,j = 1 for APCADA, CLOUDNET, LD40 DCLOUDNET,j is the observed minimum CLOUDNET cloud base height in the 10-minute period at time j uncertainty for all “Reference” algorithm

  8. Cabauw fractional cloudiness Columnn=0, 8 highn=2-7 low CLOUDNET60% n=8mainly dueto cirrus “reference” isgoodcompromise low n=2-6 (higher during day time) Cloud cover histogram

  9. Cabauw fractional cloudiness Cloudiness versus cloud base height • NubiScope & TSIgenerally bestagreement • ACPADA &LD40 lower • CLOUDNETtoo high

  10. Cabauw fractional cloudiness Contingency matrix LD40 () versus Reference () • 8 % with differences > 2 okta; fraction clear sky & overcast

  11. Cabauw fractional cloudiness Reference data set cloudiness • 98% availability10-minute cloudiness • e.g. daily with uncertainty

  12. Conclusions Reference is weighted combination of individual instruments Not a true reference, but general and robust approach that produces useful results Compromise whereby the NubiScope and TSI are considered to be a higher quality product (weight 1) than the others (height dependent weight) Uncertainty range of reference cloudiness determined from the negative and positive differences between the reference and the cloudiness reported by each instrument over the time period under consideration Findings for instruments see paper OBS also has limitations so 100% similarity not expected Automated cloudiness using ceilometer introduced changes in climatological cloud observations records Conclusions & Outlook

  13. Outlook APCADA and TSI are being / have been optimised as a result of this study Physical definition of cloud/cloudiness, threshold possibly dependent on application Usage of hemispheric method to overcome changes in climatological cloud observations records should be considered Towards scanning reference system? Conclusions & Outlook

  14. Thank you for your attention!Lookup conference paper for more informationBoers, R., M.J. de Haij, W.M.F. Wauben, H. Klein Baltink, L.H. van Ulft, M. Savenije and C.N. Long (2010), Optimized Fractional Cloudiness Determination from Five Ground - based Remote Sensing Techniques, submitted to J. Geophys. Res.

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